Analysis of Time-Based Geospatial Mashups Using Ad Hoc Visual Queries

1. A computer-implemented method for analyzing customer-based business data, comprising the steps of: streaming customer-based business data related to customers and their shopping transactions from data sources operatively coupled to the internet, including servers, computer systems and sensors, to a client computer system; displaying customer-based business data on a map or other geographic representation in a first window within a web browser on the client computer system; displaying customer-based business data on a timeline in the first or a second window within the web browser on the client computer system; linking and correlating the customer-based business data between the displays in the web browser to form mashup data; and displaying additional map layers comprising store locations wherein an additional map layer further comprises a representative store floor plan having at least one aisle and at least one shelf, and wherein the method further comprises zooming in to view sales and inventory data for a specific floor, a specific aisle, and a specific shelf of the store floor plan.

2. The method according to claim 1 , further comprising the step of displaying geospatial and temporal shopping patterns of customers associated with the customer-based business data.

3. The method according to claim 1 , wherein customers are grouped into at least three customer segments: “brand aspirationals”, “value-price shoppers”, and “price-sensitive affluents” based on customer demographics and buying habits.

4. The method according to claim 1 , wherein the time and location of purchases of specific customers may be identified in the displayed customer-based business data.

5. The method according to claim 1 , wherein the customer-based business data includes customer transactions that are time-stamped and geo-coded.

6. The method according to claim 5 , wherein the linking and correlating step further includes the step of analyzing and fusing different sources of customer-based business data.

7. The method according to claim 1 , wherein retailers identify buying patterns within or among key customer segments which drive more targeted promotions, better merchandising, improved product mix, and modified store hours for greater sales per square foot.

8. The method according to claim 1 , further comprising the step of displaying at least one planogram, and wherein product performance data is dynamically linked to each planogram, and wherein the linked planogram and product performance data is at least one of the store specific, and shelf level specific.

9. The method according to claim 8 , wherein the planogram and the product performance data is integrated with at least one of perpetual inventory systems, POS data, and RFID data and scanner data.

10. The method according to claim 1 , wherein retailers and suppliers are allowed access to the mashup data, and wherein the retailers and suppliers gauge compliance with programs by at least one of product type, location, and geography.

11. The method according to claim 10 , wherein the retailer and supplier collaborate on key performance metrics and view results in real-time using the web browser with common diagrams and visuals that are the same as in a planning process.

12. A computer-implemented method for analyzing customer-based business data, comprising the steps of: providing a web browser-based thin client for having GIS (Geographic Information System) functionality for displaying map and image data; streaming customer-based business data in the form of GML (geographic markup language, KML (Keyhole Markup Language) or GPX (GPS Tracks) data from sources operatively coupled to the internet to the thin client; displaying customer-based business data on a map or other geographic representation in a first window within a web browser-based thin client; displaying customer-based business data on a timeline in the first or a second window within the web browser-based thin client; displaying customer-based business data using visual analysis metaphors in the first, the second or a third window within the web browser-based thin client; linking and correlating the customer-based business data between the displays in the web browser-based thin client to form mashup data.

13. The method of claim 12 wherein the visual analysis metaphors include at least time wheel, node and link graphs and standard business charts.

14. The method of claim 12 wherein the linking and correlating step further includes the step of analyzing and fusing different sources of customer-based business data.

15. The method according to claim 12 , wherein the method further comprises displaying additional map layers comprising store locations.

16. The method according to claim 15 , wherein an additional map layer is a representative store floor plan having at least one aisle and at least one shelf, and wherein the method further comprises zooming in to view sales and inventory data for a specific floor, a specific aisle, and a specific shelf of the store floor plan.

17. The method according to claim 16 , further comprising the step of displaying at least one planogram, and wherein product performance data is dynamically linked to each planogram, and wherein the linked planogram and product performance data is at least one of the store specific, and shelf level specific.

18. The method according to claim 17 , wherein the planogram and the product performance data is integrated with at least one of perpetual inventory systems, POS data, and RFID data and scanner data.